



import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import DetaImageProcessor







class 				_a				(   unittest.TestCase					):

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=7,   SCREAMING_SNAKE_CASE_=3,   SCREAMING_SNAKE_CASE_=30,   SCREAMING_SNAKE_CASE_=400,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=None,   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],   SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5],   SCREAMING_SNAKE_CASE_=True,   SCREAMING_SNAKE_CASE_=1 / 255,   SCREAMING_SNAKE_CASE_=True,   )       ->   int:
						# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
						UpperCAmelCase_:		int							      =     size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
						UpperCAmelCase_:		Optional[Any]							      =     parent
						UpperCAmelCase_:		List[Any]							      =     batch_size
						UpperCAmelCase_:		Optional[Any]							      =     num_channels
						UpperCAmelCase_:		int							      =     min_resolution
						UpperCAmelCase_:		List[str]							      =     max_resolution
						UpperCAmelCase_:		str							      =     do_resize
						UpperCAmelCase_:		List[Any]							      =     size
						UpperCAmelCase_:		List[Any]							      =     do_normalize
						UpperCAmelCase_:		Optional[Any]							      =     image_mean
						UpperCAmelCase_:		Any							      =     image_std
						UpperCAmelCase_:		str							      =     do_rescale
						UpperCAmelCase_:		str							      =     rescale_factor
						UpperCAmelCase_:		List[Any]							      =     do_pad

			def      __snake_case       (self					)       ->   Dict:
						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						}

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_=False					)       ->   Dict:
						if not batched:
									UpperCAmelCase_:		List[Any]							      =     image_inputs[0]
									if isinstance(SCREAMING_SNAKE_CASE_,   Image.Image					):
												UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     image.size
									else:
												UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[Any]							      =     image.shape[1], image.shape[2]
									if w < h:
												UpperCAmelCase_:		List[Any]							      =     int(self.size["""shortest_edge"""] * h / w					)
												UpperCAmelCase_:		int							      =     self.size["""shortest_edge"""]
									elif w > h:
												UpperCAmelCase_:		List[Any]							      =     self.size["""shortest_edge"""]
												UpperCAmelCase_:		Optional[Any]							      =     int(self.size["""shortest_edge"""] * w / h					)
									else:
												UpperCAmelCase_:		List[Any]							      =     self.size["""shortest_edge"""]
												UpperCAmelCase_:		Any							      =     self.size["""shortest_edge"""]

						else:
									UpperCAmelCase_:		Union[str, Any]							      =     []
									for image in image_inputs:
												UpperCAmelCase_      ,							UpperCAmelCase_:		str							      =     self.get_expected_values([image]					)
												expected_values.append((expected_height, expected_width)					)
									UpperCAmelCase_:		List[Any]							      =     max(SCREAMING_SNAKE_CASE_,   key=lambda SCREAMING_SNAKE_CASE_					: item[0]					)[0]
									UpperCAmelCase_:		Tuple							      =     max(SCREAMING_SNAKE_CASE_,   key=lambda SCREAMING_SNAKE_CASE_					: item[1]					)[1]

						return expected_height, expected_width







@require_torch
@require_vision
class 				_a				(   _lowerCAmelCase ,		unittest.TestCase					):
			A      		=	DetaImageProcessor if is_vision_available() else None

			def      __snake_case       (self					)       ->   List[Any]:
						UpperCAmelCase_:		int							      =     DetaImageProcessingTester(self					)

			@property
			def      __snake_case       (self					)       ->   int:
						return self.image_processor_tester.prepare_image_processor_dict()

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		Any							      =     self.image_processing_class(**self.image_processor_dict					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """image_mean"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """image_std"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_normalize"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_resize"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_rescale"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """do_pad"""					)					)
						self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_,   """size"""					)					)

			def      __snake_case       (self					)       ->   Tuple:
						UpperCAmelCase_:		List[Any]							      =     self.image_processing_class.from_dict(self.image_processor_dict					)
						self.assertEqual(image_processor.size,   {"""shortest_edge""": 18, """longest_edge""": 1333}					)
						self.assertEqual(image_processor.do_pad,   SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self					)       ->   int:
						pass

			def      __snake_case       (self					)       ->   List[str]:
						# Initialize image_processing
						UpperCAmelCase_:		Dict							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random PIL images
						UpperCAmelCase_:		Any							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   Image.Image					)

						# Test not batched input
						UpperCAmelCase_:		Dict							      =     image_processing(image_inputs[0],   return_tensors="""pt"""					).pixel_values

						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_					)

						self.assertEqual(
						    encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

						# Test batched
						UpperCAmelCase_      ,							UpperCAmelCase_:		List[str]							      =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,   batched=SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Dict							      =     image_processing(SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					).pixel_values
						self.assertEqual(
						    encoded_images.shape,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ),   )

			def      __snake_case       (self					)       ->   Tuple:
						# Initialize image_processing
						UpperCAmelCase_:		Dict							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random numpy tensors
						UpperCAmelCase_:		Dict							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_,   numpify=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   np.ndarray					)

						# Test not batched input
						UpperCAmelCase_:		str							      =     image_processing(image_inputs[0],   return_tensors="""pt"""					).pixel_values

						UpperCAmelCase_      ,							UpperCAmelCase_:		Optional[int]							      =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_					)

						self.assertEqual(
						    encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

						# Test batched
						UpperCAmelCase_:		int							      =     image_processing(SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					).pixel_values

						UpperCAmelCase_      ,							UpperCAmelCase_:		int							      =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,   batched=SCREAMING_SNAKE_CASE_					)

						self.assertEqual(
						    encoded_images.shape,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ),   )

			def      __snake_case       (self					)       ->   List[Any]:
						# Initialize image_processing
						UpperCAmelCase_:		Optional[int]							      =     self.image_processing_class(**self.image_processor_dict					)
						# create random PyTorch tensors
						UpperCAmelCase_:		Tuple							      =     prepare_image_inputs(self.image_processor_tester,   equal_resolution=SCREAMING_SNAKE_CASE_,   torchify=SCREAMING_SNAKE_CASE_					)
						for image in image_inputs:
									self.assertIsInstance(SCREAMING_SNAKE_CASE_,   torch.Tensor					)

						# Test not batched input
						UpperCAmelCase_:		Dict							      =     image_processing(image_inputs[0],   return_tensors="""pt"""					).pixel_values

						UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_					)

						self.assertEqual(
						    encoded_images.shape,   (1, self.image_processor_tester.num_channels, expected_height, expected_width),   )

						# Test batched
						UpperCAmelCase_:		Optional[int]							      =     image_processing(SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					).pixel_values

						UpperCAmelCase_      ,							UpperCAmelCase_:		Any							      =     self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_,   batched=SCREAMING_SNAKE_CASE_					)

						self.assertEqual(
						    encoded_images.shape,   (
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    ),   )

			@slow
			def      __snake_case       (self					)       ->   int:
						# prepare image and target
						UpperCAmelCase_:		int							      =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
						with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""",   """r"""					) as f:
									UpperCAmelCase_:		Optional[int]							      =     json.loads(f.read()					)

						UpperCAmelCase_:		List[Any]							      =     {"""image_id""": 39769, """annotations""": target}

						# encode them
						UpperCAmelCase_:		Optional[Any]							      =     DetaImageProcessor()
						UpperCAmelCase_:		int							      =     image_processing(images=SCREAMING_SNAKE_CASE_,   annotations=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					)

						# verify pixel values
						UpperCAmelCase_:		int							      =     torch.Size([1, 3, 800, 1066]					)
						self.assertEqual(encoding["""pixel_values"""].shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

						# verify area
						UpperCAmelCase_:		int							      =     torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify boxes
						UpperCAmelCase_:		Tuple							      =     torch.Size([6, 4]					)
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		Optional[int]							      =     torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],   SCREAMING_SNAKE_CASE_,   atol=1E-3					)					)
						# verify image_id
						UpperCAmelCase_:		Optional[Any]							      =     torch.tensor([39769]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify is_crowd
						UpperCAmelCase_:		Any							      =     torch.tensor([0, 0, 0, 0, 0, 0]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify class_labels
						UpperCAmelCase_:		Dict							      =     torch.tensor([75, 75, 63, 65, 17, 17]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify orig_size
						UpperCAmelCase_:		str							      =     torch.tensor([480, 640]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify size
						UpperCAmelCase_:		Dict							      =     torch.tensor([800, 1066]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],   SCREAMING_SNAKE_CASE_					)					)

			@slow
			def      __snake_case       (self					)       ->   Tuple:
						# prepare image, target and masks_path
						UpperCAmelCase_:		Optional[Any]							      =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""					)
						with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""",   """r"""					) as f:
									UpperCAmelCase_:		Optional[Any]							      =     json.loads(f.read()					)

						UpperCAmelCase_:		Dict							      =     {"""file_name""": """000000039769.png""", """image_id""": 39769, """segments_info""": target}

						UpperCAmelCase_:		Dict							      =     pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic"""					)

						# encode them
						UpperCAmelCase_:		Union[str, Any]							      =     DetaImageProcessor(format="""coco_panoptic"""					)
						UpperCAmelCase_:		List[str]							      =     image_processing(images=SCREAMING_SNAKE_CASE_,   annotations=SCREAMING_SNAKE_CASE_,   masks_path=SCREAMING_SNAKE_CASE_,   return_tensors="""pt"""					)

						# verify pixel values
						UpperCAmelCase_:		List[Any]							      =     torch.Size([1, 3, 800, 1066]					)
						self.assertEqual(encoding["""pixel_values"""].shape,   SCREAMING_SNAKE_CASE_					)

						UpperCAmelCase_:		Any							      =     torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]					)
						self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3],   SCREAMING_SNAKE_CASE_,   atol=1E-4					)					)

						# verify area
						UpperCAmelCase_:		Any							      =     torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify boxes
						UpperCAmelCase_:		int							      =     torch.Size([6, 4]					)
						self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape,   SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		List[str]							      =     torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0],   SCREAMING_SNAKE_CASE_,   atol=1E-3					)					)
						# verify image_id
						UpperCAmelCase_:		Union[str, Any]							      =     torch.tensor([39769]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify is_crowd
						UpperCAmelCase_:		List[Any]							      =     torch.tensor([0, 0, 0, 0, 0, 0]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify class_labels
						UpperCAmelCase_:		Tuple							      =     torch.tensor([17, 17, 63, 75, 75, 93]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify masks
						UpperCAmelCase_:		str							      =     822873
						self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item(),   SCREAMING_SNAKE_CASE_					)
						# verify orig_size
						UpperCAmelCase_:		int							      =     torch.tensor([480, 640]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""],   SCREAMING_SNAKE_CASE_					)					)
						# verify size
						UpperCAmelCase_:		Dict							      =     torch.tensor([800, 1066]					)
						self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""],   SCREAMING_SNAKE_CASE_					)					)











from typing import TYPE_CHECKING

from ...utils import _LazyModule


a      :  int			     =			{'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}


if TYPE_CHECKING:
	from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
	import sys

	a      :  int			     =			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






